library(tidyverse)
library(kableExtra)
library(DT)
library(plotly)
library(readxl)
library(ggthemes)
library(maps)

library(viridis) # Color gradients
library(lubridate)
library(qtlcharts) # Interactive scatter plots 

library(randomForest) 
library(ranger)       # a faster implementation of randomForest
library(caret)        # performe many machine learning models
library(broom)  # try: `install.packages("backports")` if diretly installing broom gives an error

library(reshape2) # to use melt()

library(deSolve)
library(permimp) # for calculating conditional importance in a speedy way
# example table
# mtcars %>% 
#   kbl(caption = "Example table",
#       format = "html",
#       booktabs = TRUE,
#       row.names = TRUE,
#       escape = TRUE) %>%
#   kable_styling(bootstrap_options = c("condensed", "striped", "hover"),
#                 position = "center") %>%
#   scroll_box(height = "300px")

1 Executive Summary

Figure 1. The flow chart of the project working process

(n.d.). World Health Organization. Retrieved from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/media-resources/science-in-5/episode-26---vaccine-dosage
Ataguba, J. E., Ojo, K. O., & Ichoku, H. E. (2016). Explaining socio-economic inequalities in immunization coverage in Nigeria. Health Policy and Planning, 31(9), 1212–1224. https://doi.org/10.1093/heapol/czw053
Braveman, P., & Gottlieb, L. (2014). The social determinants of health: It’s time to consider the causes of the causes. Public Health Reports, 129(1_suppl2), 19–31. https://doi.org/10.1177/00333549141291S206
Broman, K. W. (2015). R/qtlcharts: Interactive graphics for quantitative trait locus mapping. Genetics, 199, 359–361. https://doi.org/10.1534/genetics.114.172742
Deb, P., Furceri, D., Jiménez, D., Kothari, S., Ostry, J., & Tawk, N. (2021). The effects of COVID-19 vaccines on economic activity. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4026476
Debeer, Dries, Hothorn, T., & Strobl, C. (2021). Permimp: Conditional permutation importance. Retrieved from https://CRAN.R-project.org/package=permimp
Debeer, D., & Strobl, C. (2020). Conditional permutation importance revisited [Journal Article]. BMC Bioinformatics, 21(1), 307. https://doi.org/10.1186/s12859-020-03622-2
Dewi, C. (2019). Random forest and support vector machine on features selection for regression analysis. International Journal of Innovative Computing, Information & Control: IJICIC, 15, 2027–2037.
E.Gornick, M. (2002). Committee on guidance for designing a national healthcare disparities report. In S. E. K. (Ed.), 2, MEASURING THE EFFECTS OF SOCIOECONOMIC STATUS ON HEALTH CARE. Washington (DC): National Academies Press (US). Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK221050/
Eilers, R., Krabbe, P. F., & Melker, H. E. de. (2014). Factors affecting the uptake of vaccination by the elderly in western society [Journal Article]. Prev Med, 69, 224–234. https://doi.org/10.1016/j.ypmed.2014.10.017
Glassman, A. (n.d.). The COVID-19 vaccine rollout was the fastest in global history, but low-income countries were left behind. Retrieved from https://www.cgdev.org/blog/covid-19-vaccine-rollout-was-fastest-global-history-low-income-countries-were-left-behind
Grolemund, G., & Wickham, H. (2011). Dates and times made easy with lubridate. Journal of Statistical Software, 40(3), 1–25. Retrieved from https://www.jstatsoft.org/v40/i03/
Hannah Ritchie, L. R.-G., Edouard Mathieu, & Roser, M. (2020). Coronavirus pandemic (COVID-19). Our World in Data.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22. Retrieved from https://CRAN.R-project.org/doc/Rnews/
Maleva, T. M., Kartseva, M. A., & Korzhuk, S. V. (2021). Socio-demographic determinants of COVID-19 vaccine uptake in russia in the context of mandatory vaccination of employees. Population and Economics, 5(4), 30–49. https://doi.org/10.3897/popecon.5.e77832
R Core Team. (2022). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
Robinson, D., Hayes, A., & Couch, S. (2022). Broom: Convert statistical objects into tidy tibbles. Retrieved from https://CRAN.R-project.org/package=broom
Sievert, C. (2020). Interactive web-based data visualization with r, plotly, and shiny. Chapman; Hall/CRC. Retrieved from https://plotly-r.com
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests [Journal Article]. BMC Bioinformatics, 9(1), 307. https://doi.org/10.1186/1471-2105-9-307
Tarantola, D., & Dasgupta, N. (2021). COVID-19 surveillance data: A primer for epidemiology and data science. American Journal of Public Health, 111(4), 614–619. https://doi.org/10.2105/AJPH.2020.306088
Wickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21(12), 1–20. Retrieved from http://www.jstatsoft.org/v21/i12/
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Wickham, H., & Bryan, J. (2022). Readxl: Read excel files. Retrieved from https://CRAN.R-project.org/package=readxl
Wright, M. N., & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software, 77(1), 1–17. https://doi.org/10.18637/jss.v077.i01
Xie, Y., Cheng, J., & Tan, X. (2022). DT: A wrapper of the JavaScript library ’DataTables’. Retrieved from https://CRAN.R-project.org/package=DT
Zhu, H. (2022). kableExtra: Construct complex table with ’kable’ and pipe syntax.